AnalyticsAnalyzing Customer DataThe top 7 barriers to attribution and how to overcome them

The top 7 barriers to attribution and how to overcome them

A recent article by Martech Advisor stated that In 2017, organizations will realize that attribution is not the panacea to their measurement problems’. They argue that marketers have recently realized attribution solutions are not easy to implement, and that some have begun to question whether accurate attribution can ever truly be obtained.

While I agree that some marketers may be disillusioned with what benefits attribution can offer, the notion that this will be its downfall seems a bit dramatic. It seems to be more the case that marketers are failing to overcome the common barriers to successful attribution – mostly because they aren’t aware of them.

In the first part of this series, we looked at the general challenges associated with attributing value to different marketing channels. In this article, I’ll outline the 7 principle barriers to attribution that marketers most often encounter, and how they can be tackled.

Finally, in Part 3 we’ll look at the ‘must-knows’ on how to achieve a data-driven attribution model and master the art of data-driven attribution.

Content produced in association with Fospha.

Vector graphic of a businessman with a suitcase jumping over hurdles.

1. Data exists in unintegrated silos

Examining your data is a key first step in successfully implementing an attribution model. Customers don’t distinguish between channels, and their shopping paths regularly zig-zag.

In line with this, data silos should be broken down, and channel data unified into a holistic, consistent format, to ensure that customer journeys can be tracked appropriately. A study by AdRoll & Econsultancy (2016) found that a third of companies blame disparate data sources for a lack of progress with attribution.

Breaking down these barriers inevitably unlocks significant value and bring significant efficiency gains to marketing budgets.

2. Attribution solutions don’t take into account all marketing channels

When choosing an attribution model, it is vital to look at the complete picture. Unfortunately, most models are heavily skewed towards digital channels and tend to give credit for the vast majority of conversions to online events (clicks, signups, etc.) and the most recent marketing events (promotions, email campaigns, etc.).

However, offline is expected to continue playing a significant role in the marketing mix for the foreseeable future, and an inability to accurately capture and attribution the role of offline marketing abilities will greatly skew your results.

Even those who do examine offline channels only tend to monitor offline to offline changes, whereas it is vital to integrate online to offline conversions, and vice versa, to ensure the most accurate attribution model.

3. Attribution solutions ignore brand solutions

Many marketers assign full credit for conversions to specific channels, without taking into account the role of their brand i.e. those customers who are inclined to convert simply because of their strong affinity for a brand.

As a result, the contribution of certain channels is exaggerated, which can lead to potentially detrimental budget allocation decisions.

These negative outcomes tend to be considered a failure of attribution itself, rather than being down to the exclusion of brand lift.

4. A lack of cross-functional communication

Much like data silos create barriers, teams working in a siloed fashion can hinder the success of strategies like attribution modelling.

The aforementioned study by AdRoll also found that 40% of companies surveyed are feeling overwhelmed by the complexity of data. This can in part be down to a siloed working culture, as disparate departments can make it harder for information to be passed around.

For instance, a data team may not consider that their marketing team needs information delivered in an easily digestible manner. Furthermore, when goals and KPIs don’t align across the organization as a whole, success cannot be universally measured, and attribution modelling is hindered by the lack of agreement on what ‘good’ looks like.

Investing time and effort into bringing separate teams together to create a full picture of the data and available resources can ensure that attribution benefits all parties.

For more tips on breaking down silos and implementing a holistic approach to marketing, check out our previous ClickZ article on how to create a digital mindset throughout your organization.

Digital collaboration stock image with people working around laptops with graphs and data points overlaid in the foreground.

5. Lack of resources

In a survey by Marchtech (2017), 76% of respondents agreed that they’re challenged to find the right staff to take advantage of marketing attribution.

This gap will continue to pose a challenge, and a focus on building internal skillsets and training individuals so that employees feel empowered to handle data is a step in the right direction.

Additionally, with many people being short on time and not knowing where to begin in the world of attribution, looking to outsource individuals who are experts in this area is a step in the right direction for companies wishing to bolster their attribution efforts with minimal disruption.

6. Outdated infrastructure

Even with the right team and resources in place, an outdated infrastructure can be the Achilles heel of attribution modelling. This is due to the fact that attribution needs certain tools and technologies to be successful – you wouldn’t turn up to a soccer match with a hockey ball.

Similarly, you can’t effectively carry out attribution without the right tool kit. These tools often assist in data collection, integration, visualisation, interpreting and forecasting.

If companies don’t appreciate the need for these resources, marketers will have a much harder time running an effective attribution campaign.

7. One size doesn’t fit all

The perfect mix and balance of channels varies by business, and marketers must adopt a strategy that aligns with their customers’ behaviour. However, this is easier said than done and it can become frustrating that there is no one-size-fits all attribution strategy.

Ensuring you leave room for experimentation and small changes as you move into modelling can help you learn and move closer to your goal, step by step.

There doesn’t need to be a perfect attribution model; your brand is palpable to your customer at every touchpoint, so the ability to react to each of those opportunities with the utmost relevance is vital to achieving your KPIs.


This is Part 2 in a series of articles on using data-driven attribution in your marketing campaigns. Check back next week for Part 3, where we’ll look at the ultimate goal of data-driven marketing channel attribution.

Or read the previous instalment: The challenges of attribution: Which channel produces the highest ROI?

Content produced in association with Fospha. Click here to read our collaborative content guidelines. Views and opinions expressed in this article do not necessarily reflect those of ClickZ.

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